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Data and methods

Data, and the methods used to analyze them, are the foundation for evidence-based research. Articles in this subject area discuss the value of different types of data collection, and explain important statistical and econometric methods that provide ways to summarize and present information, and to identify and quantify correlation or causality.

Quantitative policy evaluation can benefit from
a rich set of econometric methods for analyzing count data

Often, economic policies are directed toward
outcomes that are measured as counts. Examples of economic variables that
use a basic counting scale are number of children as an indicator of
fertility, number of doctor visits as an indicator of health care demand,
and number of days absent from work as an indicator of employee shirking.
Several econometric methods are available for analyzing such data, including
the Poisson and negative binomial models. They can provide useful insights
that cannot be obtained from standard linear regression models. Estimation
and interpretation are illustrated in two empirical examples.

Does formal work pay? Synthetic measurements of
taxes and benefits can help identify incentives and disincentives to formal
work

Evidence from transition economies shows that
formal work may not pay, particularly for low-wage earners. Synthetic
measurements of work disincentives, such as the formalization tax rate or
the marginal effective tax rate, confirm a significant positive correlation
between these measurements and the probability of informal work. These
measures are especially informative for impacts at lower wage levels, where
informality is highest. Policymakers who want to increase formal work can
use these measurements to determine optimal labor taxation rates for
low-wage earners and reform benefit design.

Should statistical criteria for measuring employment and
unemployment be re-examined?

Measuring employment and unemployment is essential for economic
policy. Internationally agreed measures (e.g. headcount employment and unemployment rates
based on standard definitions) enhance comparability across time and space, but changes in
real labor markets and policy agendas challenge these traditional conventions. Boundaries
between different labor market states are blurred, complicating identification. Individual
experiences in each state may vary considerably, highlighting the importance of how each
employed or unemployed person is weighted in statistical indices.

Employers can use laboratory experiments to
structure payment policies and incentive schemes

Can a company attract a different type of
employee by changing its compensation scheme? Is it sufficient to pay more
to increase employees’ motivation? Should a firm provide evaluation feedback
to employees based on their absolute or their relative performance?
Laboratory experiments can help address these questions by identifying the
causal impact of variations in personnel policy on employees’ productivity
and mobility. Although they are collected in an artificial environment, the
qualitative external validity of findings from the lab is now well
recognized.

Why do different population groups (e.g. rural
vs. urban, youth vs. elderly and men vs. women) experience the same
objective labor status differently? One hypothesis is that people are more
concerned with relative deprivation than objective deprivation and they
value their own status relative to the status of their peers—the reference
group. One way to test this hypothesis in the labor market is to measure
individual differences in labor status while controlling for characteristics
that define population groups. This measure is called “relative labor
deprivation” and can help policymakers to better understand how labor claims
are generated.

Linear regression is a powerful tool for estimating the
relationship between one variable and a set of other variables

Linear regression is a powerful tool for investigating the
relationships between multiple variables by relating one variable to a set of variables. It
can identify the effect of one variable while adjusting for other observable differences. For
example, it can analyze how wages relate to gender, after controlling for differences in
background characteristics such as education and experience. A linear regression model is
typically estimated by ordinary least squares, which minimizes the differences between the
observed sample values and the fitted values from the model. Multiple tools are available to
evaluate the model.

Estimating the causal effect of immigration on the labor
market outcomes of native workers has been a major concern in the literature. Because
immigrants decide whether and where to migrate, immigrant populations generally consist
of individuals with characteristics that differ from those of a randomly selected
sample. One solution is to focus on events such as civil wars and natural catastrophes
that generate rapid and unexpected flows of refugees into a country unrelated to their
personal characteristics, location, and employment preferences. These “natural
experiments” yield estimates that find small negative effects on native workers’
employment but not on wages.

Ignoring the large variation in firm-level
outcomes can create misunderstandings about the consequences of many
policies

Recent research has revealed enormous variation
in performance and growth among firms, which both drives and is driven by
large reallocations of inputs and outputs across firms (churning) within
industries and markets. These differences in firm-level outcomes and the
associated turnover of firms affect many economic policies (both labor- and
non-labor-oriented), on both a microeconomic and a macroeconomic scale, and
are affected by them. Properly evaluating these policies requires
familiarity with the sources and consequences of firm-level variation and
within-industry reallocation.

Measuring hours worked is important, but
different surveys can tell different stories

Work hours are key components in estimating
productivity growth and hourly wages as well as being a useful cyclical
indicator in their own right, so measuring them correctly is important. The
US Bureau of Labor Statistics (BLS) collects data on work hours in several
surveys and publishes three widely-used series that measure average weekly
hours. The series tell different stories about average weekly hours and
trends in those hours but qualitatively similar stories about the cyclical
behavior of work hours. The research summarized here explains the
differences in levels, but only some of the differences in trends.

Studies of independent contractors suggest that
workers’ effort may be more responsive to wage incentives than previously
thought

A fundamental question in economic policy is how
labor supply responds to changes in remuneration. The responsiveness of
labor supply determines the size of the employment impact and efficiency
loss of progressive income taxation. It also affects predictions about the
impacts of policies ranging from fiscal responses to business cycles to
government transfer programs. The characteristics of jobs held by
independent contractors provide an opportunity to overcome problems faced by
earlier studies and help answer this fundamental question.